Overview

Dataset statistics

Number of variables46
Number of observations94727
Missing cells66050
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.0 MiB
Average record size in memory376.0 B

Variable types

Categorical26
Numeric18
Text2

Alerts

ciudad has constant value ""Constant
operacion has constant value ""Constant
tipologia_imueble has constant value ""Constant
amueblado is highly imbalanced (75.5%)Imbalance
arico is highly imbalanced (84.2%)Imbalance
duplex is highly imbalanced (82.5%)Imbalance
estudio is highly imbalanced (81.8%)Imbalance
nueva_construccion is highly imbalanced (80.4%)Imbalance
orientacion_n is highly imbalanced (50.7%)Imbalance
ano_construccion has 55828 (58.9%) missing valuesMissing
exterior_interior has 6380 (6.7%) missing valuesMissing
n_piso has 3841 (4.1%) missing valuesMissing
ano_construccion is highly skewed (γ1 = -22.49108759)Skewed
precio_parking is highly skewed (γ1 = 52.13352341)Skewed
n_habitaciones has 2744 (2.9%) zerosZeros
n_piso has 10094 (10.7%) zerosZeros

Reproduction

Analysis started2024-02-17 23:06:00.386052
Analysis finished2024-02-17 23:06:36.691259
Duration36.31 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

a_reformar
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
77051 
1
17676 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 77051
81.3%
1 17676
 
18.7%

Length

2024-02-17T23:06:36.743340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:36.816554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 77051
81.3%
1 17676
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 77051
81.3%
1 17676
 
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77051
81.3%
1 17676
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77051
81.3%
1 17676
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77051
81.3%
1 17676
 
18.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
52246 
1
42481 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 52246
55.2%
1 42481
44.8%

Length

2024-02-17T23:06:36.891243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:36.956865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 52246
55.2%
1 42481
44.8%

Most occurring characters

ValueCountFrequency (%)
0 52246
55.2%
1 42481
44.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52246
55.2%
1 42481
44.8%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52246
55.2%
1 42481
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52246
55.2%
1 42481
44.8%

amueblado
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
3
88721 
2
 
4690
1
 
1316

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 88721
93.7%
2 4690
 
5.0%
1 1316
 
1.4%

Length

2024-02-17T23:06:37.024222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:37.090921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 88721
93.7%
2 4690
 
5.0%
1 1316
 
1.4%

Most occurring characters

ValueCountFrequency (%)
3 88721
93.7%
2 4690
 
5.0%
1 1316
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 88721
93.7%
2 4690
 
5.0%
1 1316
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 88721
93.7%
2 4690
 
5.0%
1 1316
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 88721
93.7%
2 4690
 
5.0%
1 1316
 
1.4%

ano_construccion
Real number (ℝ)

MISSING  SKEWED 

Distinct191
Distinct (%)0.5%
Missing55828
Missing (%)58.9%
Infinite0
Infinite (%)0.0%
Mean1964.6614
Minimum1
Maximum2291
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:37.181911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1900
Q11955
median1968
Q31987
95-th percentile2008
Maximum2291
Range2290
Interquartile range (IQR)32

Descriptive statistics

Standard deviation55.907842
Coefficient of variation (CV)0.028456732
Kurtosis736.8591
Mean1964.6614
Median Absolute Deviation (MAD)16
Skewness-22.491088
Sum76423362
Variance3125.6868
MonotonicityNot monotonic
2024-02-17T23:06:37.273938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1960 2311
 
2.4%
1970 2093
 
2.2%
1965 1823
 
1.9%
1900 1643
 
1.7%
1950 862
 
0.9%
1930 822
 
0.9%
1966 783
 
0.8%
1940 782
 
0.8%
2006 742
 
0.8%
1975 741
 
0.8%
Other values (181) 26297
27.8%
(Missing) 55828
58.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
7 1
 
< 0.1%
10 2
< 0.1%
19 3
< 0.1%
48 1
 
< 0.1%
49 2
< 0.1%
50 2
< 0.1%
54 1
 
< 0.1%
160 1
 
< 0.1%
173 1
 
< 0.1%
ValueCountFrequency (%)
2291 1
 
< 0.1%
2028 1
 
< 0.1%
2020 13
 
< 0.1%
2019 114
 
0.1%
2018 404
0.4%
2017 190
0.2%
2016 52
 
0.1%
2015 65
 
0.1%
2014 51
 
0.1%
2013 60
 
0.1%

area_construida
Real number (ℝ)

Distinct558
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.35174
Minimum21
Maximum985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:37.374176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile40
Q162
median83
Q3117
95-th percentile225
Maximum985
Range964
Interquartile range (IQR)55

Descriptive statistics

Standard deviation67.052528
Coefficient of variation (CV)0.66158243
Kurtosis17.684128
Mean101.35174
Median Absolute Deviation (MAD)25
Skewness3.182888
Sum9600746
Variance4496.0415
MonotonicityNot monotonic
2024-02-17T23:06:37.490527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 3128
 
3.3%
70 2974
 
3.1%
80 2603
 
2.7%
65 2416
 
2.6%
75 2348
 
2.5%
90 2159
 
2.3%
50 1969
 
2.1%
100 1927
 
2.0%
55 1744
 
1.8%
110 1573
 
1.7%
Other values (548) 71886
75.9%
ValueCountFrequency (%)
21 58
 
0.1%
22 61
 
0.1%
23 54
 
0.1%
24 74
 
0.1%
25 235
 
0.2%
26 75
 
0.1%
27 127
 
0.1%
28 158
 
0.2%
29 75
 
0.1%
30 652
0.7%
ValueCountFrequency (%)
985 1
< 0.1%
982 1
< 0.1%
951 1
< 0.1%
950 1
< 0.1%
941 2
< 0.1%
934 1
< 0.1%
928 1
< 0.1%
926 1
< 0.1%
922 1
< 0.1%
900 2
< 0.1%

arico
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
92539 
1
 
2188

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 92539
97.7%
1 2188
 
2.3%

Length

2024-02-17T23:06:37.573961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:37.641267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 92539
97.7%
1 2188
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 92539
97.7%
1 2188
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92539
97.7%
1 2188
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92539
97.7%
1 2188
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92539
97.7%
1 2188
 
2.3%

armarios
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
54169 
0
40558 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 54169
57.2%
0 40558
42.8%

Length

2024-02-17T23:06:37.716082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:37.774075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 54169
57.2%
0 40558
42.8%

Most occurring characters

ValueCountFrequency (%)
1 54169
57.2%
0 40558
42.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 54169
57.2%
0 40558
42.8%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 54169
57.2%
0 40558
42.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 54169
57.2%
0 40558
42.8%

ascensor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
65881 
0
28846 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 65881
69.5%
0 28846
30.5%

Length

2024-02-17T23:06:37.857418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:37.923964image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 65881
69.5%
0 28846
30.5%

Most occurring characters

ValueCountFrequency (%)
1 65881
69.5%
0 28846
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 65881
69.5%
0 28846
30.5%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 65881
69.5%
0 28846
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 65881
69.5%
0 28846
30.5%

buen_estado
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
74173 
0
20554 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 74173
78.3%
0 20554
 
21.7%

Length

2024-02-17T23:06:37.990780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:38.060196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 74173
78.3%
0 20554
 
21.7%

Most occurring characters

ValueCountFrequency (%)
1 74173
78.3%
0 20554
 
21.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 74173
78.3%
0 20554
 
21.7%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 74173
78.3%
0 20554
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 74173
78.3%
0 20554
 
21.7%

cat_ano_construccion
Real number (ℝ)

Distinct168
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1965.6824
Minimum1623
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:38.140755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1623
5-th percentile1900
Q11955
median1967
Q31984
95-th percentile2008
Maximum2018
Range395
Interquartile range (IQR)29

Descriptive statistics

Standard deviation29.107109
Coefficient of variation (CV)0.014807636
Kurtosis2.0924528
Mean1965.6824
Median Absolute Deviation (MAD)14
Skewness-0.82126556
Sum1.8620319 × 108
Variance847.22378
MonotonicityNot monotonic
2024-02-17T23:06:38.240276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1960 6024
 
6.4%
1965 4924
 
5.2%
1970 4684
 
4.9%
1900 3443
 
3.6%
1950 2138
 
2.3%
1940 2037
 
2.2%
1969 1978
 
2.1%
1968 1889
 
2.0%
1930 1844
 
1.9%
1966 1815
 
1.9%
Other values (158) 63951
67.5%
ValueCountFrequency (%)
1623 2
 
< 0.1%
1627 3
 
< 0.1%
1655 1
 
< 0.1%
1692 1
 
< 0.1%
1696 1
 
< 0.1%
1700 1
 
< 0.1%
1723 1
 
< 0.1%
1730 2
 
< 0.1%
1780 1
 
< 0.1%
1800 8
< 0.1%
ValueCountFrequency (%)
2018 952
1.0%
2017 431
0.5%
2016 146
 
0.2%
2015 177
 
0.2%
2014 378
 
0.4%
2013 159
 
0.2%
2012 189
 
0.2%
2011 182
 
0.2%
2010 424
0.4%
2009 592
0.6%

cat_calidad
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.8529443
Minimum0
Maximum9
Zeros377
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:38.340473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.462975
Coefficient of variation (CV)0.30146131
Kurtosis-0.049663738
Mean4.8529443
Median Absolute Deviation (MAD)1
Skewness-0.035997529
Sum459700
Variance2.1402957
MonotonicityNot monotonic
2024-02-17T23:06:38.410521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 24637
26.0%
5 20721
21.9%
6 20477
21.6%
3 12607
13.3%
7 10424
11.0%
2 2701
 
2.9%
8 1528
 
1.6%
1 627
 
0.7%
9 627
 
0.7%
0 377
 
0.4%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 377
 
0.4%
1 627
 
0.7%
2 2701
 
2.9%
3 12607
13.3%
4 24637
26.0%
5 20721
21.9%
6 20477
21.6%
7 10424
11.0%
8 1528
 
1.6%
9 627
 
0.7%
ValueCountFrequency (%)
9 627
 
0.7%
8 1528
 
1.6%
7 10424
11.0%
6 20477
21.6%
5 20721
21.9%
4 24637
26.0%
3 12607
13.3%
2 2701
 
2.9%
1 627
 
0.7%
0 377
 
0.4%

cat_n_max_pisos
Real number (ℝ)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3766086
Minimum0
Maximum26
Zeros101
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:38.490209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median6
Q38
95-th percentile12
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8455507
Coefficient of variation (CV)0.4462483
Kurtosis5.8470714
Mean6.3766086
Median Absolute Deviation (MAD)1
Skewness1.7688656
Sum604037
Variance8.0971591
MonotonicityNot monotonic
2024-02-17T23:06:38.573560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
5 21357
22.5%
4 13990
14.8%
7 13658
14.4%
6 13554
14.3%
8 9380
9.9%
3 5445
 
5.7%
9 5159
 
5.4%
10 2847
 
3.0%
2 1774
 
1.9%
11 1657
 
1.7%
Other values (16) 5906
 
6.2%
ValueCountFrequency (%)
0 101
 
0.1%
1 586
 
0.6%
2 1774
 
1.9%
3 5445
 
5.7%
4 13990
14.8%
5 21357
22.5%
6 13554
14.3%
7 13658
14.4%
8 9380
9.9%
9 5159
 
5.4%
ValueCountFrequency (%)
26 46
 
< 0.1%
25 31
 
< 0.1%
23 88
 
0.1%
22 72
 
0.1%
21 182
0.2%
20 87
 
0.1%
19 20
 
< 0.1%
18 83
 
0.1%
17 287
0.3%
16 248
0.3%

cat_n_vecinos
Real number (ℝ)

Distinct329
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.168189
Minimum1
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:38.673529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median21
Q340
95-th percentile141
Maximum1499
Range1498
Interquartile range (IQR)28

Descriptive statistics

Standard deviation54.235032
Coefficient of variation (CV)1.3846704
Kurtosis35.291023
Mean39.168189
Median Absolute Deviation (MAD)11
Skewness4.3844743
Sum3710285
Variance2941.4387
MonotonicityNot monotonic
2024-02-17T23:06:38.773528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 4863
 
5.1%
21 4095
 
4.3%
9 3963
 
4.2%
13 3334
 
3.5%
17 2874
 
3.0%
16 2742
 
2.9%
7 2521
 
2.7%
15 2461
 
2.6%
10 2419
 
2.6%
14 2391
 
2.5%
Other values (319) 63064
66.6%
ValueCountFrequency (%)
1 689
 
0.7%
2 1087
 
1.1%
3 1126
 
1.2%
4 1401
 
1.5%
5 1475
 
1.6%
6 1107
 
1.2%
7 2521
2.7%
8 1577
 
1.7%
9 3963
4.2%
10 2419
2.6%
ValueCountFrequency (%)
1499 2
 
< 0.1%
724 23
< 0.1%
701 1
 
< 0.1%
638 4
 
< 0.1%
574 55
0.1%
518 2
 
< 0.1%
512 1
 
< 0.1%
503 11
 
< 0.1%
501 10
 
< 0.1%
478 22
 
< 0.1%

ciudad
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Madrid
94727 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters568362
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMadrid
2nd rowMadrid
3rd rowMadrid
4th rowMadrid
5th rowMadrid

Common Values

ValueCountFrequency (%)
Madrid 94727
100.0%

Length

2024-02-17T23:06:38.894112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:38.973827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
madrid 94727
100.0%

Most occurring characters

ValueCountFrequency (%)
d 189454
33.3%
M 94727
16.7%
a 94727
16.7%
r 94727
16.7%
i 94727
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 473635
83.3%
Uppercase Letter 94727
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 189454
40.0%
a 94727
20.0%
r 94727
20.0%
i 94727
20.0%
Uppercase Letter
ValueCountFrequency (%)
M 94727
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 568362
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 189454
33.3%
M 94727
16.7%
a 94727
16.7%
r 94727
16.7%
i 94727
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 568362
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 189454
33.3%
M 94727
16.7%
a 94727
16.7%
r 94727
16.7%
i 94727
16.7%

distancia_castellana
Real number (ℝ)

Distinct94610
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6738112
Minimum0.0014350974
Maximum12.576566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:39.057298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.0014350974
5-th percentile0.27506176
Q11.034841
median1.9554413
Q33.8374899
95-th percentile7.1654343
Maximum12.576566
Range12.575131
Interquartile range (IQR)2.802649

Descriptive statistics

Standard deviation2.2106082
Coefficient of variation (CV)0.826763
Kurtosis1.3082797
Mean2.6738112
Median Absolute Deviation (MAD)1.1867174
Skewness1.259273
Sum253282.11
Variance4.8867884
MonotonicityNot monotonic
2024-02-17T23:06:39.173563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.036097092 2
 
< 0.1%
0.8209324245 2
 
< 0.1%
0.03723037431 2
 
< 0.1%
2.101087773 2
 
< 0.1%
2.540232454 2
 
< 0.1%
0.1003410268 2
 
< 0.1%
0.5640174007 2
 
< 0.1%
1.991045814 2
 
< 0.1%
0.6161744843 2
 
< 0.1%
0.2181938896 2
 
< 0.1%
Other values (94600) 94707
> 99.9%
ValueCountFrequency (%)
0.001435097407 1
< 0.1%
0.004269475612 1
< 0.1%
0.004322044394 1
< 0.1%
0.004934849361 1
< 0.1%
0.006173318995 1
< 0.1%
0.006305782332 1
< 0.1%
0.007929002888 1
< 0.1%
0.008422856558 1
< 0.1%
0.008501845916 1
< 0.1%
0.008559026743 1
< 0.1%
ValueCountFrequency (%)
12.57656576 1
< 0.1%
12.57358266 1
< 0.1%
12.5640167 1
< 0.1%
12.55972806 1
< 0.1%
12.55785999 1
< 0.1%
12.5537872 1
< 0.1%
12.55331639 1
< 0.1%
12.5372153 1
< 0.1%
12.53622285 1
< 0.1%
12.5339924 1
< 0.1%

distancia_metro
Real number (ℝ)

Distinct94348
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47583134
Minimum0.0014160887
Maximum9.4252139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:39.273340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.0014160887
5-th percentile0.096084154
Q10.21345594
median0.33178915
Q30.52269052
95-th percentile1.2127204
Maximum9.4252139
Range9.4237978
Interquartile range (IQR)0.30923457

Descriptive statistics

Standard deviation0.61123909
Coefficient of variation (CV)1.2845709
Kurtosis48.649124
Mean0.47583134
Median Absolute Deviation (MAD)0.13995634
Skewness6.0511933
Sum45074.076
Variance0.37361323
MonotonicityNot monotonic
2024-02-17T23:06:39.373517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07262400891 3
 
< 0.1%
0.01007800048 3
 
< 0.1%
0.1351915238 3
 
< 0.1%
0.03547740611 3
 
< 0.1%
0.2222319847 2
 
< 0.1%
0.1651912968 2
 
< 0.1%
0.08921237025 2
 
< 0.1%
0.177538052 2
 
< 0.1%
0.1141615568 2
 
< 0.1%
0.1649805838 2
 
< 0.1%
Other values (94338) 94703
> 99.9%
ValueCountFrequency (%)
0.001416088655 2
< 0.1%
0.002588903776 1
< 0.1%
0.003159330755 1
< 0.1%
0.004017688228 1
< 0.1%
0.004132945902 1
< 0.1%
0.004134038551 1
< 0.1%
0.004135130912 1
< 0.1%
0.004376046082 1
< 0.1%
0.004477056832 1
< 0.1%
0.004687972017 1
< 0.1%
ValueCountFrequency (%)
9.42521385 1
< 0.1%
9.374053603 1
< 0.1%
9.355168276 1
< 0.1%
9.344541354 1
< 0.1%
9.341095837 1
< 0.1%
9.334654359 1
< 0.1%
9.329856913 1
< 0.1%
9.329834263 1
< 0.1%
8.98225003 1
< 0.1%
8.969796872 1
< 0.1%

distancia_puerta_sol
Real number (ℝ)

Distinct94616
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4791039
Minimum0.0076465716
Maximum14.158814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:39.474114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.0076465716
5-th percentile0.7594512
Q12.4059496
median4.1215871
Q36.207808
95-th percentile9.1714949
Maximum14.158814
Range14.151168
Interquartile range (IQR)3.8018584

Descriptive statistics

Standard deviation2.678492
Coefficient of variation (CV)0.5979973
Kurtosis-0.12621153
Mean4.4791039
Median Absolute Deviation (MAD)1.8387287
Skewness0.60836381
Sum424292.08
Variance7.1743196
MonotonicityNot monotonic
2024-02-17T23:06:39.976192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.39488528 2
 
< 0.1%
6.232652246 2
 
< 0.1%
2.229995798 2
 
< 0.1%
2.517067299 2
 
< 0.1%
1.960711682 2
 
< 0.1%
0.8994113456 2
 
< 0.1%
8.678312755 2
 
< 0.1%
3.172303465 2
 
< 0.1%
12.24669974 2
 
< 0.1%
1.13621151 2
 
< 0.1%
Other values (94606) 94707
> 99.9%
ValueCountFrequency (%)
0.007646571605 1
< 0.1%
0.01537414325 1
< 0.1%
0.01703500572 1
< 0.1%
0.01994969235 1
< 0.1%
0.02541720017 1
< 0.1%
0.02546122988 1
< 0.1%
0.02558615768 1
< 0.1%
0.02854700518 1
< 0.1%
0.02911750986 1
< 0.1%
0.03200618324 1
< 0.1%
ValueCountFrequency (%)
14.15881423 1
< 0.1%
14.15776049 1
< 0.1%
14.1499303 1
< 0.1%
14.1436442 1
< 0.1%
14.13952592 1
< 0.1%
14.13862493 1
< 0.1%
14.13033589 1
< 0.1%
14.12645899 1
< 0.1%
14.12090709 1
< 0.1%
14.12023961 1
< 0.1%

duplex
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
92236 
1
 
2491

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 92236
97.4%
1 2491
 
2.6%

Length

2024-02-17T23:06:40.073508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:40.139969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 92236
97.4%
1 2491
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 92236
97.4%
1 2491
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92236
97.4%
1 2491
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92236
97.4%
1 2491
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92236
97.4%
1 2491
 
2.6%

estudio
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
92116 
1
 
2611

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 92116
97.2%
1 2611
 
2.8%

Length

2024-02-17T23:06:40.223387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:40.289746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 92116
97.2%
1 2611
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 92116
97.2%
1 2611
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92116
97.2%
1 2611
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92116
97.2%
1 2611
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92116
97.2%
1 2611
 
2.8%

exterior_interior
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing6380
Missing (%)6.7%
Memory size1.4 MiB
1.0
76245 
2.0
12102 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters265041
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 76245
80.5%
2.0 12102
 
12.8%
(Missing) 6380
 
6.7%

Length

2024-02-17T23:06:40.356678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:40.427343image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 76245
86.3%
2.0 12102
 
13.7%

Most occurring characters

ValueCountFrequency (%)
. 88347
33.3%
0 88347
33.3%
1 76245
28.8%
2 12102
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176694
66.7%
Other Punctuation 88347
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 88347
50.0%
1 76245
43.2%
2 12102
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 88347
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 265041
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 88347
33.3%
0 88347
33.3%
1 76245
28.8%
2 12102
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 265041
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 88347
33.3%
0 88347
33.3%
1 76245
28.8%
2 12102
 
4.6%

fecha
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
201812
44237 
201803
21893 
201809
15958 
201806
12639 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters568362
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201803
2nd row201803
3rd row201803
4th row201803
5th row201803

Common Values

ValueCountFrequency (%)
201812 44237
46.7%
201803 21893
23.1%
201809 15958
 
16.8%
201806 12639
 
13.3%

Length

2024-02-17T23:06:40.507473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:40.589808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
201812 44237
46.7%
201803 21893
23.1%
201809 15958
 
16.8%
201806 12639
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 145217
25.6%
2 138964
24.4%
1 138964
24.4%
8 94727
16.7%
3 21893
 
3.9%
9 15958
 
2.8%
6 12639
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 568362
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 145217
25.6%
2 138964
24.4%
1 138964
24.4%
8 94727
16.7%
3 21893
 
3.9%
9 15958
 
2.8%
6 12639
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 568362
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 145217
25.6%
2 138964
24.4%
1 138964
24.4%
8 94727
16.7%
3 21893
 
3.9%
9 15958
 
2.8%
6 12639
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 568362
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 145217
25.6%
2 138964
24.4%
1 138964
24.4%
8 94727
16.7%
3 21893
 
3.9%
9 15958
 
2.8%
6 12639
 
2.2%
Distinct75738
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:40.745283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length21
Median length20
Mean length20.39408
Min length17

Characters and Unicode

Total characters1931870
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61922 ?
Unique (%)65.4%

Sample

1st rowA15019136831406238029
2nd rowA6677225905472065344
3rd rowA13341979748618524775
4th rowA4775182175615276542
5th rowA2492087730711701973
ValueCountFrequency (%)
a5463639993615125363 11
 
< 0.1%
a14882068007191593522 9
 
< 0.1%
a2282202115281541721 9
 
< 0.1%
a1315840462730187222 8
 
< 0.1%
a9716330137639818420 7
 
< 0.1%
a6151307694369968367 7
 
< 0.1%
a14940791098683555615 7
 
< 0.1%
a17351323363085385366 7
 
< 0.1%
a9865685988976540204 7
 
< 0.1%
a9858360437524013306 7
 
< 0.1%
Other values (75728) 94648
99.9%
2024-02-17T23:06:41.014979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 224421
11.6%
2 181596
9.4%
3 181406
9.4%
4 180702
9.4%
5 180588
9.3%
6 180336
9.3%
7 180190
9.3%
8 177562
9.2%
0 175609
9.1%
9 174733
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1837143
95.1%
Uppercase Letter 94727
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 224421
12.2%
2 181596
9.9%
3 181406
9.9%
4 180702
9.8%
5 180588
9.8%
6 180336
9.8%
7 180190
9.8%
8 177562
9.7%
0 175609
9.6%
9 174733
9.5%
Uppercase Letter
ValueCountFrequency (%)
A 94727
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1837143
95.1%
Latin 94727
 
4.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 224421
12.2%
2 181596
9.9%
3 181406
9.9%
4 180702
9.8%
5 180588
9.8%
6 180336
9.8%
7 180190
9.8%
8 177562
9.7%
0 175609
9.6%
9 174733
9.5%
Latin
ValueCountFrequency (%)
A 94727
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1931870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 224421
11.6%
2 181596
9.4%
3 181406
9.4%
4 180702
9.4%
5 180588
9.3%
6 180336
9.3%
7 180190
9.3%
8 177562
9.2%
0 175609
9.1%
9 174733
9.0%

jardin
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
77277 
1
17450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 77277
81.6%
1 17450
 
18.4%

Length

2024-02-17T23:06:41.125452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:41.192631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 77277
81.6%
1 17450
 
18.4%

Most occurring characters

ValueCountFrequency (%)
0 77277
81.6%
1 17450
 
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77277
81.6%
1 17450
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77277
81.6%
1 17450
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77277
81.6%
1 17450
 
18.4%

latitud
Real number (ℝ)

Distinct94616
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.421097
Minimum40.328682
Maximum40.520637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:41.272663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum40.328682
5-th percentile40.367331
Q140.396866
median40.423288
Q340.441939
95-th percentile40.476524
Maximum40.520637
Range0.19195462
Interquartile range (IQR)0.045073001

Descriptive statistics

Standard deviation0.033411157
Coefficient of variation (CV)0.0008265772
Kurtosis-0.26902701
Mean40.421097
Median Absolute Deviation (MAD)0.022718215
Skewness0.031478973
Sum3828969.2
Variance0.0011163054
MonotonicityNot monotonic
2024-02-17T23:06:41.393252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.35801787 2
 
< 0.1%
40.4384144 2
 
< 0.1%
40.43659987 2
 
< 0.1%
40.4375113 2
 
< 0.1%
40.43407708 2
 
< 0.1%
40.40928353 2
 
< 0.1%
40.46930339 2
 
< 0.1%
40.44482727 2
 
< 0.1%
40.47114143 2
 
< 0.1%
40.40741781 2
 
< 0.1%
Other values (94606) 94707
> 99.9%
ValueCountFrequency (%)
40.32868222 1
< 0.1%
40.32870614 1
< 0.1%
40.33165199 1
< 0.1%
40.33169949 1
< 0.1%
40.33212122 1
< 0.1%
40.33212891 1
< 0.1%
40.33214619 1
< 0.1%
40.33222099 1
< 0.1%
40.33231457 1
< 0.1%
40.33241042 1
< 0.1%
ValueCountFrequency (%)
40.52063684 1
< 0.1%
40.52034852 1
< 0.1%
40.5202431 1
< 0.1%
40.52006389 1
< 0.1%
40.51987313 1
< 0.1%
40.51986685 1
< 0.1%
40.51983257 1
< 0.1%
40.51975356 1
< 0.1%
40.51959642 1
< 0.1%
40.51946819 1
< 0.1%
Distinct135
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:41.572692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length32
Median length27
Mean length11.765178
Min length3

Characters and Unicode

Total characters1114480
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPau de Carabanchel
2nd rowPalacio
3rd rowPalacio
4th rowPalacio
5th rowPalacio
ValueCountFrequency (%)
san 5574
 
3.8%
de 4972
 
3.4%
lavapiés-embajadores 3716
 
2.6%
del 3403
 
2.3%
3165
 
2.2%
malasaña-universidad 2577
 
1.8%
puerta 2258
 
1.6%
vallecas 2091
 
1.4%
goya 2042
 
1.4%
vista 1972
 
1.4%
Other values (166) 113647
78.2%
2024-02-17T23:06:41.855797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 161493
14.5%
e 97208
 
8.7%
s 83569
 
7.5%
r 66372
 
6.0%
i 63124
 
5.7%
l 62563
 
5.6%
o 61099
 
5.5%
50690
 
4.5%
n 46422
 
4.2%
d 36757
 
3.3%
Other values (47) 385183
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 901055
80.8%
Uppercase Letter 145788
 
13.1%
Space Separator 50690
 
4.5%
Dash Punctuation 16419
 
1.5%
Decimal Number 528
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 161493
17.9%
e 97208
10.8%
s 83569
9.3%
r 66372
 
7.4%
i 63124
 
7.0%
l 62563
 
6.9%
o 61099
 
6.8%
n 46422
 
5.2%
d 36757
 
4.1%
t 34310
 
3.8%
Other values (20) 188138
20.9%
Uppercase Letter
ValueCountFrequency (%)
C 18305
12.6%
P 16077
11.0%
A 12937
 
8.9%
V 12902
 
8.8%
L 10437
 
7.2%
S 8797
 
6.0%
E 8737
 
6.0%
B 8069
 
5.5%
M 6668
 
4.6%
G 6173
 
4.2%
Other values (13) 36686
25.2%
Decimal Number
ValueCountFrequency (%)
1 264
50.0%
2 264
50.0%
Space Separator
ValueCountFrequency (%)
50690
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1046843
93.9%
Common 67637
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 161493
15.4%
e 97208
 
9.3%
s 83569
 
8.0%
r 66372
 
6.3%
i 63124
 
6.0%
l 62563
 
6.0%
o 61099
 
5.8%
n 46422
 
4.4%
d 36757
 
3.5%
t 34310
 
3.3%
Other values (43) 333926
31.9%
Common
ValueCountFrequency (%)
50690
74.9%
- 16419
 
24.3%
1 264
 
0.4%
2 264
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1087689
97.6%
None 26791
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 161493
14.8%
e 97208
 
8.9%
s 83569
 
7.7%
r 66372
 
6.1%
i 63124
 
5.8%
l 62563
 
5.8%
o 61099
 
5.6%
50690
 
4.7%
n 46422
 
4.3%
d 36757
 
3.4%
Other values (39) 358392
32.9%
None
ValueCountFrequency (%)
é 7387
27.6%
ñ 6085
22.7%
í 4583
17.1%
ó 4128
15.4%
Á 2362
 
8.8%
ü 1191
 
4.4%
á 587
 
2.2%
ú 468
 
1.7%

longitud
Real number (ℝ)

Distinct94616
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.6864265
Minimum-3.8336106
Maximum-3.5408378
Zeros0
Zeros (%)0.0%
Negative94727
Negative (%)100.0%
Memory size1.4 MiB
2024-02-17T23:06:41.972611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-3.8336106
5-th percentile-3.7474881
Q1-3.7084777
median-3.6941247
Q3-3.6662477
95-th percentile-3.6120073
Maximum-3.5408378
Range0.29277278
Interquartile range (IQR)0.042229969

Descriptive statistics

Standard deviation0.03910987
Coefficient of variation (CV)-0.010609155
Kurtosis0.6756366
Mean-3.6864265
Median Absolute Deviation (MAD)0.020835071
Skewness0.40730476
Sum-349204.13
Variance0.0015295819
MonotonicityNot monotonic
2024-02-17T23:06:42.072514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.593575859 2
 
< 0.1%
-3.63605706 2
 
< 0.1%
-3.705037589 2
 
< 0.1%
-3.715057312 2
 
< 0.1%
-3.706555726 2
 
< 0.1%
-3.699260327 2
 
< 0.1%
-3.779258617 2
 
< 0.1%
-3.698744153 2
 
< 0.1%
-3.578256193 2
 
< 0.1%
-3.697909275 2
 
< 0.1%
Other values (94606) 94707
> 99.9%
ValueCountFrequency (%)
-3.833610625 1
< 0.1%
-3.833107748 1
< 0.1%
-3.832932814 1
< 0.1%
-3.832706688 1
< 0.1%
-3.832533869 1
< 0.1%
-3.832513838 1
< 0.1%
-3.832442618 1
< 0.1%
-3.832430377 1
< 0.1%
-3.827888505 1
< 0.1%
-3.827372412 1
< 0.1%
ValueCountFrequency (%)
-3.540837846 1
< 0.1%
-3.540910906 1
< 0.1%
-3.541013201 1
< 0.1%
-3.54102911 1
< 0.1%
-3.541071002 1
< 0.1%
-3.541099714 1
< 0.1%
-3.541146808 1
< 0.1%
-3.541271311 1
< 0.1%
-3.541372653 1
< 0.1%
-3.54138271 1
< 0.1%

n_banos
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5845852
Minimum0
Maximum20
Zeros89
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:42.158644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84180368
Coefficient of variation (CV)0.53124546
Kurtosis14.965519
Mean1.5845852
Median Absolute Deviation (MAD)0
Skewness2.4379687
Sum150103
Variance0.70863344
MonotonicityNot monotonic
2024-02-17T23:06:42.239031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 53554
56.5%
2 31331
33.1%
3 6659
 
7.0%
4 2121
 
2.2%
5 709
 
0.7%
6 159
 
0.2%
0 89
 
0.1%
7 39
 
< 0.1%
8 28
 
< 0.1%
11 15
 
< 0.1%
Other values (8) 23
 
< 0.1%
ValueCountFrequency (%)
0 89
 
0.1%
1 53554
56.5%
2 31331
33.1%
3 6659
 
7.0%
4 2121
 
2.2%
5 709
 
0.7%
6 159
 
0.2%
7 39
 
< 0.1%
8 28
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
11 15
< 0.1%
10 7
 
< 0.1%
9 7
 
< 0.1%
8 28
< 0.1%

n_habitaciones
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5801514
Minimum0
Maximum93
Zeros2744
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:42.339010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum93
Range93
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.24454
Coefficient of variation (CV)0.48235156
Kurtosis302.18944
Mean2.5801514
Median Absolute Deviation (MAD)1
Skewness4.9823257
Sum244410
Variance1.5488799
MonotonicityNot monotonic
2024-02-17T23:06:42.422307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 33944
35.8%
2 28402
30.0%
1 13332
 
14.1%
4 11655
 
12.3%
5 3325
 
3.5%
0 2744
 
2.9%
6 789
 
0.8%
7 279
 
0.3%
8 142
 
0.1%
9 35
 
< 0.1%
Other values (11) 80
 
0.1%
ValueCountFrequency (%)
0 2744
 
2.9%
1 13332
 
14.1%
2 28402
30.0%
3 33944
35.8%
4 11655
 
12.3%
5 3325
 
3.5%
6 789
 
0.8%
7 279
 
0.3%
8 142
 
0.1%
9 35
 
< 0.1%
ValueCountFrequency (%)
93 1
 
< 0.1%
33 1
 
< 0.1%
20 2
 
< 0.1%
18 2
 
< 0.1%
16 2
 
< 0.1%
15 3
 
< 0.1%
14 5
 
< 0.1%
13 6
 
< 0.1%
12 18
< 0.1%
11 16
< 0.1%

n_piso
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing3841
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean2.7477279
Minimum-1
Maximum11
Zeros10094
Zeros (%)10.7%
Negative936
Negative (%)1.0%
Memory size1.4 MiB
2024-02-17T23:06:42.492461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum11
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2572566
Coefficient of variation (CV)0.8214993
Kurtosis1.5992504
Mean2.7477279
Median Absolute Deviation (MAD)1
Skewness1.1593948
Sum249730
Variance5.0952072
MonotonicityNot monotonic
2024-02-17T23:06:42.572361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 20325
21.5%
2 17033
18.0%
3 14740
15.6%
4 11625
12.3%
0 10094
10.7%
5 6158
 
6.5%
6 3729
 
3.9%
7 2441
 
2.6%
8 1492
 
1.6%
11 1051
 
1.1%
Other values (3) 2198
 
2.3%
(Missing) 3841
 
4.1%
ValueCountFrequency (%)
-1 936
 
1.0%
0 10094
10.7%
1 20325
21.5%
2 17033
18.0%
3 14740
15.6%
4 11625
12.3%
5 6158
 
6.5%
6 3729
 
3.9%
7 2441
 
2.6%
8 1492
 
1.6%
ValueCountFrequency (%)
11 1051
 
1.1%
10 449
 
0.5%
9 813
 
0.9%
8 1492
 
1.6%
7 2441
 
2.6%
6 3729
 
3.9%
5 6158
 
6.5%
4 11625
12.3%
3 14740
15.6%
2 17033
18.0%

nueva_construccion
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
91849 
1
 
2878

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 91849
97.0%
1 2878
 
3.0%

Length

2024-02-17T23:06:42.655711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:42.722978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 91849
97.0%
1 2878
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 91849
97.0%
1 2878
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91849
97.0%
1 2878
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91849
97.0%
1 2878
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91849
97.0%
1 2878
 
3.0%

operacion
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
SALE
94727 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters378908
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSALE
2nd rowSALE
3rd rowSALE
4th rowSALE
5th rowSALE

Common Values

ValueCountFrequency (%)
SALE 94727
100.0%

Length

2024-02-17T23:06:42.788884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:42.856049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
sale 94727
100.0%

Most occurring characters

ValueCountFrequency (%)
S 94727
25.0%
A 94727
25.0%
L 94727
25.0%
E 94727
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 378908
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 94727
25.0%
A 94727
25.0%
L 94727
25.0%
E 94727
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 378908
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 94727
25.0%
A 94727
25.0%
L 94727
25.0%
E 94727
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 378908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 94727
25.0%
A 94727
25.0%
L 94727
25.0%
E 94727
25.0%

orientacion_e
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
75534 
1
19193 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75534
79.7%
1 19193
 
20.3%

Length

2024-02-17T23:06:42.922179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:42.988960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 75534
79.7%
1 19193
 
20.3%

Most occurring characters

ValueCountFrequency (%)
0 75534
79.7%
1 19193
 
20.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 75534
79.7%
1 19193
 
20.3%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 75534
79.7%
1 19193
 
20.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 75534
79.7%
1 19193
 
20.3%

orientacion_n
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
84513 
1
10214 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 84513
89.2%
1 10214
 
10.8%

Length

2024-02-17T23:06:43.072256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:43.138921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 84513
89.2%
1 10214
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 84513
89.2%
1 10214
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 84513
89.2%
1 10214
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 84513
89.2%
1 10214
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 84513
89.2%
1 10214
 
10.8%

orientacion_o
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
80670 
1
14057 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 80670
85.2%
1 14057
 
14.8%

Length

2024-02-17T23:06:43.205631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:43.271976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 80670
85.2%
1 14057
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 80670
85.2%
1 14057
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 80670
85.2%
1 14057
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 80670
85.2%
1 14057
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 80670
85.2%
1 14057
 
14.8%

orientacion_s
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
72358 
1
22369 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72358
76.4%
1 22369
 
23.6%

Length

2024-02-17T23:06:43.341517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:43.405653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 72358
76.4%
1 22369
 
23.6%

Most occurring characters

ValueCountFrequency (%)
0 72358
76.4%
1 22369
 
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72358
76.4%
1 22369
 
23.6%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72358
76.4%
1 22369
 
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72358
76.4%
1 22369
 
23.6%

parking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
73439 
1
21288 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%

Length

2024-02-17T23:06:43.488804image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:43.555638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%

Most occurring characters

ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
73439 
1
21288 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%

Length

2024-02-17T23:06:43.622106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:43.705342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%

Most occurring characters

ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73439
77.5%
1 21288
 
22.5%

piscina
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
80723 
1
14004 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 80723
85.2%
1 14004
 
14.8%

Length

2024-02-17T23:06:43.772328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:43.839138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 80723
85.2%
1 14004
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 80723
85.2%
1 14004
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 80723
85.2%
1 14004
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 80723
85.2%
1 14004
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 80723
85.2%
1 14004
 
14.8%

portero
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
71103 
1
23624 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 71103
75.1%
1 23624
 
24.9%

Length

2024-02-17T23:06:43.921983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:43.987902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 71103
75.1%
1 23624
 
24.9%

Most occurring characters

ValueCountFrequency (%)
0 71103
75.1%
1 23624
 
24.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71103
75.1%
1 23624
 
24.9%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 71103
75.1%
1 23624
 
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 71103
75.1%
1 23624
 
24.9%

precio
Real number (ℝ)

Distinct2761
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395940.45
Minimum21000
Maximum8133000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:44.071834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum21000
5-th percentile97000
Q1160000
median262000
Q3467000
95-th percentile1134000
Maximum8133000
Range8112000
Interquartile range (IQR)307000

Descriptive statistics

Standard deviation417075.03
Coefficient of variation (CV)1.0533782
Kurtosis29.012852
Mean395940.45
Median Absolute Deviation (MAD)124000
Skewness4.0423995
Sum3.7506251 × 1010
Variance1.7395158 × 1011
MonotonicityNot monotonic
2024-02-17T23:06:44.188240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137000 448
 
0.5%
127000 416
 
0.4%
128000 411
 
0.4%
132000 400
 
0.4%
130000 391
 
0.4%
158000 378
 
0.4%
138000 377
 
0.4%
147000 376
 
0.4%
162000 369
 
0.4%
157000 367
 
0.4%
Other values (2751) 90794
95.8%
ValueCountFrequency (%)
21000 1
 
< 0.1%
24000 3
< 0.1%
25000 1
 
< 0.1%
26000 1
 
< 0.1%
28000 1
 
< 0.1%
29000 3
< 0.1%
30000 1
 
< 0.1%
32000 1
 
< 0.1%
33000 3
< 0.1%
34000 1
 
< 0.1%
ValueCountFrequency (%)
8133000 1
< 0.1%
7138000 1
< 0.1%
7124000 1
< 0.1%
7044000 1
< 0.1%
7018000 1
< 0.1%
6996000 1
< 0.1%
6970000 1
< 0.1%
6848000 1
< 0.1%
6829000 1
< 0.1%
6729000 1
< 0.1%

precio_logaritmico
Real number (ℝ)

Distinct2761
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.563197
Minimum9.9522777
Maximum15.91144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:44.288481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum9.9522777
5-th percentile11.482466
Q111.982929
median12.4761
Q313.054085
95-th percentile13.941262
Maximum15.91144
Range5.9591627
Interquartile range (IQR)1.0711554

Descriptive statistics

Standard deviation0.7601302
Coefficient of variation (CV)0.06050452
Kurtosis0.028814249
Mean12.563197
Median Absolute Deviation (MAD)0.52748006
Skewness0.52989108
Sum1190073.9
Variance0.57779792
MonotonicityNot monotonic
2024-02-17T23:06:44.407788image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.8277362 448
 
0.5%
11.75194237 416
 
0.4%
11.75978554 411
 
0.4%
11.7905572 400
 
0.4%
11.77528973 391
 
0.4%
11.97035031 378
 
0.4%
11.83500896 377
 
0.4%
11.89818787 376
 
0.4%
11.99535161 369
 
0.4%
11.96400108 367
 
0.4%
Other values (2751) 90794
95.8%
ValueCountFrequency (%)
9.952277717 1
 
< 0.1%
10.08580911 3
< 0.1%
10.1266311 1
 
< 0.1%
10.16585182 1
 
< 0.1%
10.23995979 1
 
< 0.1%
10.27505111 3
< 0.1%
10.30895266 1
 
< 0.1%
10.37349118 1
 
< 0.1%
10.40426284 3
< 0.1%
10.4341158 1
 
< 0.1%
ValueCountFrequency (%)
15.91144042 1
< 0.1%
15.78094318 1
< 0.1%
15.77897992 1
< 0.1%
15.76768675 1
< 0.1%
15.76398884 1
< 0.1%
15.76084912 1
< 0.1%
15.75712578 1
< 0.1%
15.7394672 1
< 0.1%
15.73668881 1
< 0.1%
15.7219371 1
< 0.1%

precio_parking
Real number (ℝ)

SKEWED 

Distinct146
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean719.69068
Minimum1
Maximum925001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:44.508785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum925001
Range925000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7515.8103
Coefficient of variation (CV)10.443112
Kurtosis5059.7506
Mean719.69068
Median Absolute Deviation (MAD)0
Skewness52.133523
Sum68174139
Variance56487404
MonotonicityNot monotonic
2024-02-17T23:06:44.605001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 92539
97.7%
20001 230
 
0.2%
30001 210
 
0.2%
25001 185
 
0.2%
15001 183
 
0.2%
40001 141
 
0.1%
50001 136
 
0.1%
45001 87
 
0.1%
35001 76
 
0.1%
60001 62
 
0.1%
Other values (136) 878
 
0.9%
ValueCountFrequency (%)
1 92539
97.7%
2 9
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
11 2
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
26 3
 
< 0.1%
41 4
 
< 0.1%
51 5
 
< 0.1%
ValueCountFrequency (%)
925001 1
 
< 0.1%
770001 1
 
< 0.1%
750001 1
 
< 0.1%
510001 1
 
< 0.1%
450001 1
 
< 0.1%
275001 1
 
< 0.1%
250001 1
 
< 0.1%
231001 2
< 0.1%
220001 1
 
< 0.1%
150001 3
< 0.1%

precio_unitario_m2
Real number (ℝ)

Distinct31123
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3661.0239
Minimum805.30973
Maximum9997.561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-17T23:06:44.705837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum805.30973
5-th percentile1426.6667
Q12239.8347
median3479.1667
Q34744.6809
95-th percentile6774.1273
Maximum9997.561
Range9192.2512
Interquartile range (IQR)2504.8461

Descriptive statistics

Standard deviation1700.8025
Coefficient of variation (CV)0.46457018
Kurtosis0.2326398
Mean3661.0239
Median Absolute Deviation (MAD)1250.5631
Skewness0.72177669
Sum3.4679781 × 108
Variance2892729.2
MonotonicityNot monotonic
2024-02-17T23:06:44.821486image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 373
 
0.4%
3000 236
 
0.2%
5000 211
 
0.2%
4000 201
 
0.2%
2200 161
 
0.2%
1500 158
 
0.2%
1800 149
 
0.2%
3500 148
 
0.2%
2500 140
 
0.1%
2600 131
 
0.1%
Other values (31113) 92819
98.0%
ValueCountFrequency (%)
805.3097345 1
< 0.1%
805.5555556 2
< 0.1%
805.9701493 1
< 0.1%
806.4516129 1
< 0.1%
807.0175439 1
< 0.1%
808.2191781 1
< 0.1%
808.3333333 1
< 0.1%
808.8235294 1
< 0.1%
809.0909091 1
< 0.1%
810.5263158 1
< 0.1%
ValueCountFrequency (%)
9997.560976 1
< 0.1%
9994.285714 1
< 0.1%
9993.377483 1
< 0.1%
9992.248062 1
< 0.1%
9991.150442 1
< 0.1%
9990.909091 1
< 0.1%
9984.924623 1
< 0.1%
9979.310345 1
< 0.1%
9975 2
< 0.1%
9973.913043 1
< 0.1%

terraza
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
61096 
1
33631 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 61096
64.5%
1 33631
35.5%

Length

2024-02-17T23:06:44.904989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:44.971678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 61096
64.5%
1 33631
35.5%

Most occurring characters

ValueCountFrequency (%)
0 61096
64.5%
1 33631
35.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61096
64.5%
1 33631
35.5%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61096
64.5%
1 33631
35.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61096
64.5%
1 33631
35.5%

tipologia_imueble
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
HOME
94727 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters378908
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHOME
2nd rowHOME
3rd rowHOME
4th rowHOME
5th rowHOME

Common Values

ValueCountFrequency (%)
HOME 94727
100.0%

Length

2024-02-17T23:06:45.055101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:45.120988image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
home 94727
100.0%

Most occurring characters

ValueCountFrequency (%)
H 94727
25.0%
O 94727
25.0%
M 94727
25.0%
E 94727
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 378908
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 94727
25.0%
O 94727
25.0%
M 94727
25.0%
E 94727
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 378908
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 94727
25.0%
O 94727
25.0%
M 94727
25.0%
E 94727
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 378908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 94727
25.0%
O 94727
25.0%
M 94727
25.0%
E 94727
25.0%

trastero
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
70233 
1
24494 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94727
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 70233
74.1%
1 24494
 
25.9%

Length

2024-02-17T23:06:45.188076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T23:06:45.254785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 70233
74.1%
1 24494
 
25.9%

Most occurring characters

ValueCountFrequency (%)
0 70233
74.1%
1 24494
 
25.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94727
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 70233
74.1%
1 24494
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
Common 94727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 70233
74.1%
1 24494
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 70233
74.1%
1 24494
 
25.9%

Interactions

2024-02-17T23:06:33.862381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:04.487195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.217898image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:07.751683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.333778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.789017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.640582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:14.396075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:16.390069image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:18.180379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:20.082061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:21.946684image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:23.495435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:25.516104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.211874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:28.688759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:30.862682image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.397117image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:33.942142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:04.572979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.301605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:07.851010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.417102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.886584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.725549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:14.513674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:16.489666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:18.286231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:20.161956image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.021749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:23.603463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:25.624266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.301075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:29.162291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:30.947248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.478679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.022263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:04.651906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.384443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:07.934130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.500762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.986408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.799172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:14.620287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:16.589234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:18.357741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:20.268873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.095999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:23.703305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:25.718987image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.377706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:29.244604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.026593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.559557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.110691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:04.752282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.495977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:08.034784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.587986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:11.083009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.903511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:14.763358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:16.718756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:18.452997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:20.441200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.197170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:23.812352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:25.825563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.468141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:29.361296image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.123464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.660686image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.178354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:04.818525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.567883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:08.117822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.650167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:11.166722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.992607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:14.848889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:16.811561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:18.550574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:20.518125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.278968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:23.929713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:25.919806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.545606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:29.464194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.195870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.725912image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.258632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:04.919158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.651160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:08.200663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.736947image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:11.503404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:13.076566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:14.968385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:16.914611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:18.630375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:20.615948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.344606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:24.031426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:26.019701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.612209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:29.547997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.276229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.812788image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.341999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:05.001937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.737845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:08.294147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.816894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:11.594805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:13.165009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:15.072271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:17.019732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:18.729381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:20.701451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.448639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:24.147598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:26.121529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.710778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:29.635880image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.359609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.897017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.427818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:05.087981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.818233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:08.385268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.900526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:11.686405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:13.252546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:15.188585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:17.116251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:18.813856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:20.836490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.541037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:24.267734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:26.231938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.796392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:29.732265image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.460028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.975846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.492419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:05.151891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.902258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:08.450511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.966753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:11.766321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:13.337830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:15.290620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:17.232002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:18.914794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:20.980175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.622557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:24.370711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:26.313285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.859431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:29.808921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.546296image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:33.042236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.593427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:05.239177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.984457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:08.550788image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.054698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:11.856808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:13.425096image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:15.401147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:17.349597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:19.016477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:21.084257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.713915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:24.493008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:26.429144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.943959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:29.992924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.629549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:33.125549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.676115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:05.338844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:07.067745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:08.650673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.151339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:11.953513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:13.522125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:15.529153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:17.465813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:19.098957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:21.180838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.812449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:24.611578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:26.535274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:28.042669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:30.110342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.733488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:33.225992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.758456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:05.418215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:07.191025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:08.733894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.216657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.033059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:13.612195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:15.646281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:17.603830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:19.178859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:21.280489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.881373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:24.718146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:26.611258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:28.127451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:30.220258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.815132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:33.310297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.842222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:05.535083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:07.268802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:08.833833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.299932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.116281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:13.707311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:15.759486image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:17.703166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:19.277877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:21.380394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:22.980542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:24.825698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:26.710505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:28.211611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:30.329205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.909231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:33.395672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.928869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:05.818085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:07.350898image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:08.917102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.391299image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.203893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:13.788558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:15.843804image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:17.787882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:19.648963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:21.485713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:23.063831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:24.932108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:26.776518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:28.299308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:30.420289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:31.976434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:33.474770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:34.991619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:05.902269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:07.434461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.003989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.469929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.295911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:13.896897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:15.956766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:17.868187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:19.731476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:21.581073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:23.146228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:25.047583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:26.862695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:28.360098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:30.493526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.059584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:33.542023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:35.095008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:05.989822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:07.517710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.096653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.552969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.391002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:14.073445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:16.070767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:17.947088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:19.831779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:21.664490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:23.246007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:25.173767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:26.962071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:28.459155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:30.593893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.158608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:33.625975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:35.176291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.067985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:07.601035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.185979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.637242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.478619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:14.200146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:16.190260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:18.032815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:19.930515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:21.765499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:23.330526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:25.303205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.047147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:28.547625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:30.685028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.225972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:33.718524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:35.241612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:06.151557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:07.683491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:09.267166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:10.700027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:12.560333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:14.308364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:16.292269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:18.113630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:20.013873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:21.846630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:23.429869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:25.418041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:27.128894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:28.609101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:30.762991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:32.320114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T23:06:33.791381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-02-17T23:06:35.420943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-17T23:06:35.994087image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

a_reformaraire_acondicionadoamuebladoano_construccionarea_construidaaricoarmariosascensorbuen_estadocat_ano_construccioncat_calidadcat_n_max_pisoscat_n_vecinosciudaddistancia_castellanadistancia_metrodistancia_puerta_solduplexestudioexterior_interiorfechaid_anunciojardinlatitudlocation_namelongitudn_banosn_habitacionesn_pisonueva_construccionoperacionorientacion_eorientacion_norientacion_oorientacion_sparkingparking_incluido_preciopiscinaporteroprecioprecio_logaritmicoprecio_parkingprecio_unitario_m2terrazatipologia_imuebletrastero
01132005.047011020053.07319Madrid6.8686770.8720758.058429001.0201803A15019136831406238029140.362485Pau de Carabanchel-3.766933111.00SALE0000001112600011.74403712680.8510640HOME1
1003NaN54010119003.0511Madrid1.5441250.1163820.876369002.0201803A6677225905472065344040.422430Palacio-3.710725111.00SALE0000000023500012.36734114351.8518520HOME0
2013NaN75010119153.0626Madrid1.6084440.1391090.907479001.0201803A13341979748618524775040.422190Palacio-3.711571123.00SALE0001000037300012.82933414973.3333330HOME1
3013NaN48001119475.0915Madrid1.5161660.1442990.845462002.0201803A4775182175615276542040.422251Palacio-3.710440111.00SALE0000000028400012.55673015916.6666670HOME0
40031930.050000119307.0519Madrid1.7941360.3370981.250231011.0201803A2492087730711701973040.408741Palacio-3.714340100.00SALE0000000022800012.33710114560.0000000HOME0
5103NaN127001019003.0518Madrid1.1681260.1614360.541773001.0201803A18372428154681111419040.412639Lavapiés-Embajadores-3.707522233.00SALE0000000049800013.11835513921.2598430HOME0
6003NaN35001119423.0615Madrid1.5174370.1269950.859565012.0201803A4705946410795464036040.422450Palacio-3.710395102.00SALE0000000022500012.32385616428.5714290HOME0
7003NaN100001119606.0626Madrid1.7629220.2634451.346115001.0201803A8243762537477781718040.407409Imperial-3.714126124.00SALE0000000136500012.80765313650.0000001HOME0
80011900.070001119001.0516Madrid1.5483100.4371910.753575001.0201803A9587449507628658013040.414870Palacio-3.712390112.00SALE0010110042500012.95984416071.4285710HOME0
9013NaN360011119723.0715Madrid1.6094660.3126160.779228001.0201803A3694300518337702967040.417236Palacio-3.712945349.00SALE11111101318700014.97459118852.7777781HOME1
a_reformaraire_acondicionadoamuebladoano_construccionarea_construidaaricoarmariosascensorbuen_estadocat_ano_construccioncat_calidadcat_n_max_pisoscat_n_vecinosciudaddistancia_castellanadistancia_metrodistancia_puerta_solduplexestudioexterior_interiorfechaid_anunciojardinlatitudlocation_namelongitudn_banosn_habitacionesn_pisonueva_construccionoperacionorientacion_eorientacion_norientacion_oorientacion_sparkingparking_incluido_preciopiscinaporteroprecioprecio_logaritmicoprecio_parkingprecio_unitario_m2terrazatipologia_imuebletrastero
948050132007.085001120075.0367Madrid8.3924380.2943559.503915111.0201812A6905855462280780825140.402823Ambroz-3.593133201.00SALE0010110017400012.06681112047.0588240HOME0
948060132010.0120011120055.0367Madrid8.3805440.2700049.494048101.0201812A7034851435465452892140.402615Ambroz-3.593296230.00SALE0010110022900012.34147711908.3333331HOME0
948070132004.0107011120047.0820Madrid7.3270700.5014798.476247001.0201812A10324853219120899544140.400215Valdebernardo - Valderribas-3.606131235.00SALE0010111029300012.58792812738.3177570HOME1
94808013NaN33011120084.011Madrid4.8574310.5548906.549843011.0201812A8974485819301533463040.437287Simancas-3.631436102.00SALE0000111019600012.18587015939.3939390HOME0
94809013NaN103011119984.0574Madrid7.3741660.5088778.759108001.0201812A8870664464301737677140.428977Rosas-3.601721123.00SALE0100110033800012.73080113281.5533981HOME0
94810013NaN115011120093.0758Madrid8.2769500.86682610.003059001.0201812A3962186799478940177140.445810Rejas-3.592154231.00SALE0000111034700012.75708013017.3913041HOME1
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